Oil and gas reservoirs are underground formations of rock containing oil and/or gas. The type and properties of the rock vary by reservoir and within reservoirs. For example, a porosity and a permeability of reservoir rock may vary from well to well within a reservoir. The porosity is the percentage of pore volume, or void space, within the reservoir rock that can contain fluids. The permeability is an estimate of the ability of reservoir rock to permit the flow of fluids.
Many oil and gas wells in the United States produce from low-permeability, shale, or “tight” reservoirs. These reservoirs present many challenges in drilling, completions, and reservoir evaluation. In order to produce at economic rates, low-permeability wells must be completed by a stimulation treatment, such as hydraulic fracturing. A typical fracture treatment represents a significant fraction of the total cost of drilling and completing the well. Hence, whether or not a fracture treatment will be economically productive is a question of great interest to the operator.
In conventional reservoirs, determining the success of a stimulation treatment is performed by conducting and analyzing a buildup test or other type of pressure transient test after the treatment is applied to the reservoir. The rate at which a pressure transient moves through a reservoir is a function of the permeability. As a result, low-permeability reservoirs require long test times to sample a significant portion of the reservoir. Further, it is now known that the shut-in associated with the build-up test can significantly harm a well's productivity. Therefore, pressure transient tests are of limited application for hydraulically fractured wells in low-permeability reservoirs, where weeks or years are required to obtain usable pressure data and thereby increasing operation costs.
In an attempt to reduce costs, production of a well in the reservoir may be estimated prior to the proposed stimulation treatment or in order to select the best possible new well locations to maximize profitability. Production of oil, gas, and/or byproducts thereof from a well is usually estimated by analyzing production data. Because direct measurement of future production data is not possible in forecasting overall production, the production estimations are frequently unreliable.
The prior art has attempted to solve the problem of unreliable estimates with limited success. For example, U.S. Pat. No. 7,225,078 to Shelley et al. discloses a system and method for predicting production of a well. The system collects and processes data from a set of wells of a reservoir to generate a production prediction model for the set of wells. The data collected are logs from the set of wells, including MRI logs. The system clusters the log data from various wells based on similar predetermined characteristics, preferably by the MRI log, to generate a set of log profiles. The set of log profiles are correlated with validation indicators. The system optimizes the log profiles by reducing or adding the number of clusters in a log profile to obtain or approach a linear alignment of the log profile with the validation indicators. A set of production indicators is associated with each log profile. The set of production indicators may be based on average swab test results or a subset of the validation indicators. The optimized set of log profiles and associated production indicators is stored as a production prediction model. However, the system and method in Shelley cannot evaluate the reliability of the prediction model.
U.S. Pat. No. 7,369,979 to Spivey discloses a method for forecasting performance of wells in multilayer reservoirs having commingled production. A multi-layer predictive model is first calculated including a fluid property model, a tubing pressure gradient model, and a single layer predictive model. A non-linear regression module is used to generate synthetic models to compare to observed data. The method begins by collecting data a well in a reservoir. The fluid property model is calculated from the data over a predetermined time. The tubing pressure gradient model is calculated from the fluid property model. A single layer prediction model is calculated for each layer in the reservoir, thereby generating the multi-layer predictive model. A synthetic production history and a set of synthetic production log data are generated using the multi-layer prediction model and the non-linear regression module to compare with an observed production history and an observed production log data, respectively. However, like Shelley, the prediction model in Spivey does not include any means for calculating the accuracy of the prediction model.
The prior art does not disclose or suggest a system and method for analyzing and validating oil and gas well production data. Therefore, there is a need in the art for a system and method for predicting the production of oil and gas wells and verifying the accuracy of the prediction. Especially for shale wells, there is a need for the interpretability of water data.